Belief State Planning for Autonomously Navigating Urban Intersections

Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.Comment: 6 pages, 6 figures, accepted to IV201

Using Support Vector Machines and Bayesian Filtering for Classifying Agent Intentions at Road Intersections

Classifying other agents’ intentions is a very complex task but it can be very essential in assisting (autonomous or human) agents in navigating safely in dynamic and possibly hostile environments. This paper introduces a classification approach based on support vector machines and Bayesian filtering (SVM-BF). It then applies it to a road intersection problem to assist a vehicle in detecting the intention of an approaching suspicious vehicle. The SVM-BF approach achieved very promising results.Ford Motor Company, Le Fonds Quebecois de la Recherche sur la Nature et les Technologies (FQRNT

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018

Research Subpoenas and the Sociology of Knowledge

Jasanoff says that the most effective way to integrate scientific knowledge fully and fairly into legal decisionmaking may be for judges to develop a keener sense of how science works

Faculty Excellence

Each year, the University of New Hampshire selects a small number of its outstanding faculty for special recognition of their achievements in teaching, scholarship and service. Awards for Excellence in Teaching are given in each college and school, and university-wide awards recognize public service, research, teaching and engagement. This booklet details the year\u27s award winners\u27 accomplishments in short profiles with photographs and text

Faculty Excellence

Each year, the University of New Hampshire selects a small number of its outstanding faculty for special recognition of their achievements in teaching, scholarship and service. Awards for Excellence in Teaching are given in each college and school, and university-wide awards recognize public service, research, teaching and engagement. This booklet details the year\u27s award winners\u27 accomplishments in short profiles with photographs and text

Nobel Lecture: LIGO and gravitational waves III

The first observation of gravitational waves, by LIGO on September 14, 2015, was the culmination of a near half century effort by ∼1200 scientists and engineers of the LIGO/Virgo Collaboration. It was also the remarkable beginning of a whole new way to observe the universe: gravitational astronomy. The Nobel Prize for “decisive contributions” to this triumph was awarded to only three members of the Collaboration: Rainer Weiss, Barry Barish, and me. But, in fact, it is the entire collaboration that deserves the primary credit. For this reason, in accepting the Nobel Prize, I regard myself as an icon for the Collaboration